Name
Abstract | ||
|---|---|---|
Despite the increasing interest towards session-based and streaming recommender systems, there is still a lack of publicly available evaluation frameworks supporting both these paradigms. To address the gap, we propose FlowRec — an extension of the streaming framework Scikit-Multiflow, which opens plentiful possibilities for prototyping recommender systems operating on sessionized data streams, thanks to the underlying collection of incremental learners and support for real-time performance tracking. We describe the extended functionalities of the adapted prequential evaluation protocol, and develop a competitive recommendation algorithm on top of Scikit-Multiflow’s implementation of a Hoeffding Tree. We compare our algorithm to other known baselines for the next-item prediction task across three different domains. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-47426-3_6 | PAKDD (1) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitris Paraschakis | 1 | 9 | 2.91 |
| Bengt J. Nilsson | 2 | 210 | 24.43 |